Dose Prediction in Proton Cancer Therapy based on Density Maps from Dual-energy CT Using Joint Statistical Image Reconstruction.

Accurate range prediction is critical in proton therapy to ensure conformal tumor dose. Currently, a safety margin of 2-3.5% is added to the end of the clinical target volume (CTV) to account for uncertainties in proton range estimated from single-energy CT (SECT) stopping-power ratio (SPR) maps. Our lab previously introduced and tested a joint statistical image reconstruction method (JSIR) using a basis-vector model (BVM) for SPR-map estimation which outperforms competing dual-energy CT (DECT) methods in both simulated and experimental scenarios. However, its impact on clinical proton-therapy treatment planning has yet to be demonstrated. In this study, we introduce a workflow for interfacing accurate JSIR-BVM density maps to a commercially-available Monte Carlo-based proton-therapy treatment-planning system, and compare the resultant dose-prediction errors to those of a standard SECT stoichiometric calibration technique for simulated and clinical patient cases. Percentage deviation from the ground-truth volume receiving 80% of the prescription dose within a 5 mm distal-ring region around the planning target volume was 2.6% for JSIR-BVM and 6.8% for SECT in the simulated case, showing a nontrivial risk of overshooting to surrounding tissues in the case of SECT. For the clinical head-and-neck cancer patient case, the mean errors in a similarly-defined ROI were 2.35% and 13.86%, for JSIR-BVM and SECT, respectively.